http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Processing metabolomics and proteomics data with open software
Metabolomics and proteomics allow deep insights into the chemistry and physiological processes of biological systems. This book will enable researchers, practitioners and students from different backgrounds to analyze metabolomics and proteomics mass spectrometry data.
Intro -- Half Title -- Series editors -- Title -- Copyright -- Preface -- Contents -- Part A General Section -- Chapter 1 Introduction -- 1.1 Hypothesis-driven versus Exploratory Research -- 1.2 Mass Spectrometry Basics -- 1.2.1 The Sample Introduction Unit -- 1.2.2 The Separation/Imaging Component -- 1.2.3 The Ionization Unit -- 1.2.4 The Mass Analyzer -- 1.2.5 Fragmentation -- 1.2.6 Detector -- 1.2.7 Mass Spectra and Mass Chromatograms -- 1.2.8 LC-MS Analysis and Data Acquisition Strategies -- 1.3 Why Open Software for Mass Spectrometry? -- References
Chapter 2 Mass Spectrometry Data Operations and Workflows -- 2.1 Operations -- 2.1.1 Formatting -- 2.1.2 Alignment -- 2.1.3 Peak Detection -- 2.1.4 Identification -- 2.1.5 Calibration -- 2.1.6 Quantification -- 2.1.7 Quality Control -- 2.1.8 Statistical Analysis -- 2.1.9 Visualization -- 2.1.10 Deposition -- 2.2 Workflows -- References -- Chapter 3 Metabolomics -- 3.1 Introduction to Metabolomics -- 3.2 Different 'Flavours' of Metabolomics -- 3.3 Technologies for Metabolomics -- 3.3.1 LC-MS and LC-MS/MS for Metabolomics -- 3.3.2 GC-MS for Metabolomics -- 3.3.3 CE-MS for Metabolomics
3.4 LC-MS Processes and Software for Metabolomics -- 3.4.1 Untargeted LC-MS Metabolomics Tools and Workflows -- 3.4.2 Targeted LC-MS Metabolomics Tools and Workflows -- 3.5 GC-MS Metabolomics Tools and Workflows -- 3.6 CE-MS Metabolomics Workflows and Software -- 3.6.1 Data Pre-processing Software -- 3.6.2 Statistical Analysis -- 3.6.3 Metabolite Annotation -- 3.7 Lipidomics Workflows and Software Tools -- 3.7.1 LC-MS Lipidomics Software -- 3.7.2 Shotgun Lipidomics -- 3.7.3 Imaging Lipidomics of Mass Spectrometry Imaging -- 3.8 Conclusion -- References -- Chapter 4 Proteomics
4.1 The Proteome: Dimensions, Scales, and Complexity -- 4.2 Proteomic Experiments and Data Life Cycle -- 4.3 Signal Processing -- 4.4 Qualitative Analysis -- 4.5 Quantitative Analysis -- 4.6 Getting the Bigger Picture -- References -- Chapter 5 Statistics, Data Mining and Modeling -- 5.1 Sample Comparison -- 5.1.1 Distance Measures -- 5.1.2 Multiple Sample Visualization -- 5.1.3 Outlier Detection -- 5.2 Dimensionality Reduction -- 5.2.1 Principal Component Analysis -- 5.2.2 Self-organizing Maps -- 5.3 Cluster Analyses -- 5.3.1 K-Means -- 5.3.2 Hierarchical Clustering -- 5.4 Important Variables
5.4.1 Ranking Peaks -- 5.4.2 Biomarker Discovery -- 5.5 Predictive Models -- 5.5.1 Machine Learning Introduction -- 5.5.2 Supervised Learning Models -- 5.5.3 Dataset Partitioning Methods -- 5.5.4 Performance Measures -- 5.5.5 A Classification Case Study -- Acknowledgements -- References -- Part B Open MS Programs, Toolkits and Workflow Platforms -- Chapter 6 OpenMS and KNIME for Mass Spectrometry Data Processing -- 6.1 Introduction -- 6.2 OpenMS for Developers -- 6.2.1 C++ Library -- 6.2.2 Data Formats and Raw Data API -- 6.2.3 Algorithms -- 6.2.4 TOPP Tools (Developer Perspective)
Cambridge : Royal Society of Chemistry, 2020
New developments in mass spectrometry ; 8